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--- |
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library_name: peft |
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language: |
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- en |
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license: apache-2.0 |
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base_model: openai/whisper-small |
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tags: |
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- generated_from_trainer |
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datasets: |
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- mesolitica/IMDA-TTS |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Small NSC small (500 steps) - Jarrett Er |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: NSC Small section |
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type: mesolitica/IMDA-TTS |
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split: None |
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args: 'config: en, split: train' |
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metrics: |
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- type: wer |
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value: 3.0164184803360063 |
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name: Wer |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Whisper Small NSC small (500 steps) - Jarrett Er |
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This model is a fine-tuned version of [Thecoder3281f/whisper-small-hi-commonvoice17-1000](https://huggingface.co/Thecoder3281f/whisper-small-hi-commonvoice17-1000) on the NSC Small section dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0777 |
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- Wer: 3.0164 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 50 |
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- training_steps: 500 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:------:|:----:|:---------------:|:------:| |
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| 0.0822 | 0.8850 | 100 | 0.0686 | 3.0164 | |
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| 0.0585 | 1.7699 | 200 | 0.0700 | 3.0928 | |
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| 0.0317 | 2.6549 | 300 | 0.0726 | 3.0546 | |
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| 0.0184 | 3.5398 | 400 | 0.0781 | 3.2455 | |
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| 0.0194 | 4.4248 | 500 | 0.0777 | 3.0164 | |
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### Framework versions |
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- PEFT 0.14.0 |
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- Transformers 4.45.2 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.2.1.dev0 |
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- Tokenizers 0.20.3 |
